جست‌وجوی استاد و پژوهشگر

• Structural Health Monitoring
• Seismic Risk Assessment
• Cultural Heritage Conservation
• +۹ مورد دیگر

Erica Lenticchia is a Fixed-term Assistant Professor and researcher in the Department of Structural, Building and Geotechnical Engineering (DISEG) at the Polytechnic University of Turin. She is a member of the Interdepartmental Center R3C – Responsible Risk Resilience Center, contributing to research and teaching in civil and structural engineering with a focus on architectural and cultural heritage. Her academic appointments are within the College of Architecture and Design, where she collaborates across multiple degree programs. Research Interests: Structural Health Monitoring Seismic Risk Assessment of Cultural Heritage Durability of Cementitious and Historical Materials Dynamic Identification and Structural Dynamics Resilience and Vulnerability Assessment of Historical Centers Digital Twin Applications in Heritage Management Her recent publications reflect a strong trend in experimental and computational structural assessment, particularly on ferrocement durability, model updating techniques, and seismic resilience of built heritage. These works span high-impact journals and international conferences, showcasing interdisciplinary collaboration and innovation. Scientific Awards and Intellectual Contributions: Co-inventor of the national and international patent CAMELOT (Structural Model Corroboration Toolbox) Co-developer of associated software for structural model validation Teaching and Supervision: Erica Lenticchia actively contributes to teaching in both undergraduate and graduate programs, including: PhD Course: Seismic Risk of Cultural Heritage Master’s Courses: Design and Optimization of Shells and Spatial Structures, Integrated Restoration and Conservation, Adaptive Reuse of the Built Legacy Bachelor’s Courses: Construction Techniques, Atelier Design-Build, Construction Workshop E She serves as a PhD supervisor for Gerardo Sorrentino in the Civil and Environmental Engineering program. Her educational roles span architecture, civil engineering, and sustainability-focused curricula, emphasizing interdisciplinary integration. Research Projects: DiTHiCe (2025–2027): Digital Twin Development for Resilient Management of Minor Historical Centres – Project Manager CAMELOT - PoC Transition (2023–2024): PRIN-funded research – Member of Research Group Commercial Project on Mont Blanc Tunnel Wall Verification (2024–2025): Scientific Manager These projects highlight her leadership in both competitive and applied research domains.

Claudio Casettiمشاهده پروفایل

استاد

• Vehicular Networks
• Intelligent Transportation Systems
• 5G/6G Networks
• +۶ مورد دیگر

Claudio Casetti is a Full Professor at the Department of Control and Computer Engineering, Politecnico di Torino, Italy. His academic career spans over 250 peer-reviewed publications in vehicular networks, Intelligent Transportation Systems (ITS), 5G/6G networks, and IoT systems, with an H-index of 42. He serves as Editor-in-Chief of IEEE Vehicular Technology Magazine since July 2024 and was previously Senior Editor (2020-2024) and Associate Editor (2013-2020). A Senior Member of IEEE and Distinguished Lecturer by the IEEE Vehicular Technology Society (2024), he leads research units in projects like 5G-CARMEN, Hexa-X, and CONNECT. Department of Control and Computer Engineering, Politecnico di Torino Editor-in-Chief, IEEE Vehicular Technology Magazine (2024-present) Senior Member, IEEE Distinguished Lecturer, IEEE Vehicular Technology Society (2024) His research focuses on vehicular networks, 5G/6G systems, IoT for smart cities, and edge computing. Recent work includes digital twins for V2X communication, AI-driven network scheduling, and low-cost C-ITS solutions. He has pioneered cooperative perception systems and latency-sensitive task management in cloud-edge environments. He has received recognition as an IEEE Senior Member and Distinguished Lecturer. His editorial leadership includes roles at IEEE Vehicular Technology Magazine, and he has organized tutorials and conferences like IEEE WiMob and CCNC. Casetti has led projects such as the Turin Urban Digital Mobility working group (2018-2022), CNIT research units, and international collaborations with institutions like TU Berlin and Hasso-Plattner-Institut. His conference involvement spans chairing and program committee roles, including Technical Program Co-Chair for IEEE WiMob 2024 and General Chair for IEEE CCNC 2022.

Eric Medvetمشاهده پروفایل

دانشیار

• Evolutionary Computation
• Machine Learning
• Grammatical Evolution
• +۳ مورد دیگر

Eric Medvet is an Associate Professor of Computer Engineering at the Department of Engineering and Architecture (DIA), University of Trieste, Italy. He leads the Evolutionary Robotics and Artificial Life Lab and co-leads the Machine Learning Lab. His research focuses on Evolutionary Computation, Machine Learning, and their applications to robotics, including Grammatical Evolution, Genetic Programming, and soft robotics. He received the Google Faculty Research Award 2019 for a project on modular soft robots and co-authored a best paper at EuroGP 2024. His recent work emphasizes interpretable control policies, quality-diversity optimization, and evolutionary learning of formal specifications. Medvet teaches advanced courses on machine learning, evolutionary robotics, and programming, consistently updating curricula to reflect cutting-edge research. His labs are hubs for interdisciplinary projects, blending theory with practical implementations in robotics and AI. Research Interests: Medvet’s research spans evolutionary algorithms applied to robotics, with a focus on modular systems. He explores how genetic programming and grammatical evolution can optimize robotic designs and controllers. His work on ‘totipotent neural controllers’ and ‘body-brain co-evolution’ exemplifies efforts to create adaptable, specialized robots. He also investigates formal methods (e.g., STL specifications) for system validation and interpretable AI models. Recent trends in his publications reflect a shift toward practical applications, such as wearable movement analysis and sim-to-real transfer in robotics. Awards: Google Faculty Research Award 2019, EuroGP 2024 Best Paper Award Labs: Evolutionary Robotics and Artificial Life Lab, Machine Learning Lab Teaching: Courses include Advanced Programming, Introduction to Machine Learning, and Evolutionary Robotics, with a focus on Java and practical software development. Medvet’s approach integrates theoretical rigor with hands-on experimentation, emphasizing open-source tools like JGEA and 2D-VSR-Sim. His work bridges computational intelligence and real-world robotics challenges, driving innovation in both fields.

Flavio Corradiniمشاهده پروفایل

استاد

• Blockchain
• Process Mining
• Business Process Modeling
• +۵ مورد دیگر

Flavio Corradini is a Full Professor at the Department of Computer Science, University of Camerino. His research focuses on blockchain, process mining, business process modeling, IoT systems, and digital twins. He has led projects like the SAFE platform for disaster response and ChorChain for blockchain-based process execution. His work bridges formal methods with practical applications in robotics, smart contracts, and environmental monitoring. Education details are not explicitly provided in the text, but his academic contributions span over a decade of publications in top journals and conferences. His research often combines theoretical rigor with real-world implementation, such as developing tools like BPMN Inspector and the μG graph neural network library. Research interests include: blockchain interoperability, process choreography, IoT system engineering, and formal verification of distributed systems. His work on digital twins integrates sensor data with simulation models for industrial and environmental applications. Recent articles highlight advancements in robotic process mining, smart contract analysis, and multimodal data clustering. He has contributed to frameworks like Tangramob for urban mobility simulation and FloWare for IoT application development.

• Mathematical Finance
• Computational Finance
• Actuarial Science
• +۵ مورد دیگر

Antonio Luciano Martire is a Researcher at Sapienza University of Rome, affiliated with the Department of Methods and Models for Economy, Territory, and Finance within the Faculty of Economics. He teaches courses including Computer Science and Excel Laboratory for Business and Quantitative Finance, with office hours held on Wednesdays from 11-12 AM. His educational background includes a Bachelor's Degree in Mathematics (2008), Doctorate in Mathematics for Economic and Financial Applications (2012), and a Specialization Diploma in Applied Econometrics (2014), all from Sapienza University of Rome. Martire's research focuses on mathematical finance, computational methods, and actuarial science, with particular expertise in Volterra integral equations, options pricing models, and quantitative finance applications. His work bridges theoretical mathematics with practical financial applications, developing numerical methods for complex financial instruments and insurance products. His recent publications (2020-2024) demonstrate a consistent research trajectory in developing numerical solutions for integral equations with applications to financial derivatives, insurance products, and cryptocurrency markets. The research shows increasing sophistication in computational approaches, including neural network applications to fractional calculus problems. Martire has extensive teaching experience, having taught Financial Mathematics Laboratory and Quantitative Finance courses since 2013. His technical expertise includes scientific software (Matlab, Mathematica, R, STATA) and programming languages (C/C++).

Lea Petrellaمشاهده پروفایل

استاد

• Quantile regression
• Graphical models
• Random Forest
• +۳ مورد دیگر

Lea Petrella is a Full Professor at the Department of Methods and Models for Economics, Territory, and Finance, Sapienza University of Rome. She teaches courses in Time Series Analysis and Advanced Statistical Methods , focusing on practical applications using R software. Research Interests: Quantile regression, Graphical models, Hidden Markov Models, Risk measures, and Time Series analysis Key Projects: Generalized Dynamic Graphical Models for pandemic impacts, Penalized quantile regression for risk assessment, Multivariate quantile regression frameworks Her recent publications include: 2025: Mid-quantile mixed graphical models for public shootings 2025: Spatial quantile random forests for economic mobility 2024: Expectile hidden Markov models for cryptocurrency returns 2024: Mixed-frequency quantile regressions for risk forecasting She supervises postdocs and PhD students including Maria Saiz, Beatrice Foroni, and Valentina Raponi. Her work spans financial risk modeling, environmental statistics, and biomedical applications. Email: Lea.Petrella@uniroma1.it or lea.petrella@uniroma1.it

• Wireless Networks
• Vehicular Communications
• Network Coding
• +۷ مورد دیگر

Qingyang Song is a professor in the field of Computer Science and Engineering, focusing on wireless networks and vehicular communications. His research spans topics such as network selection, resource allocation, and fault-tolerant routing mechanisms across heterogeneous systems, including UMTS, wireless LAN, and vehicular networks. While his institutional affiliations are not explicitly stated in the provided text, his collaborative work with researchers like A. Jamalipour and Lei Guo highlights his contributions to next-generation network optimization. His research interests include Network Coding , Software Defined Networking (SDN) , 5G Systems , and Energy Efficiency . He has pioneered techniques to balance user preferences with network conditions, reducing handoffs and improving connectivity. Recent work explores UAV-assisted networks, quantum-inspired reinforcement learning for wireless VR, and secrecy rate optimization in IRS-aided SWIPT systems. Qingyang Song's publications reflect a strong emphasis on Engineering and Computer Science disciplines, particularly in vehicular communications, spectrum sharing, and hybrid routing frameworks. His work has been featured in IEEE journals and conferences, including IEEE Transactions on Vehicular Technology and IEEE Wireless Communications .

Roberto Marmoمشاهده پروفایل

استاد

• Computer Vision
• Artificial Intelligence
• Social Media Mining
• +۴ مورد دیگر

Roberto Marmo is a Professor of Computer Science at the University of Pavia since the 2022/23 academic year. His research spans multiple domains including Artificial Intelligence , Computer Vision , and Social Media Mining . He earned his PhD in Information Engineering at Pavia's Computer Vision and Multimedia Lab after completing a Computer Science degree at the University of Salerno. Core Expertise: Data Science, AI Algorithms, Scientific Visualization Industry Applications: Anti-fraud Systems, Tourism Analytics, Transportation Sign Recognition His academic work demonstrates consistent interdisciplinary innovation: 2020: Encyclopedia contributions on Cybersecurity and Social Engineering 2017: Market analysis through social media mining 2010: Social network architecture research Key collaborations include: IBM's Web 2.0 Security Encyclopedia Italian Tourist Railways Association (application of IT to heritage tourism) Academic-Industry Partnerships in transportation sign recognition His technical contributions include: Neural network applications in geological analysis Operational risk visualization frameworks SOS gesture recognition systems e-learning performance dashboards Available for consulting and training services in AI, data analysis, and scientific visualization.

• Bioinformatics
• Computational Biology
• Network Analysis
• +۴ مورد دیگر

Simona Ester Rombo is a Full Professor in the Department of Mathematics and Computer Science at Università degli Studi di Palermo (UNIPA), Italy. Her research spans computational biology, bioinformatics, network analysis, and big data approaches applied to biological and social systems. Her primary research interests include: Bioinformatics and computational genomics, particularly focusing on lncRNA-disease associations and RNA editing Network analysis and graph algorithms for biological and social networks Development of software tools for large-scale data analysis in biology Machine learning applications in drug discovery and repurposing Big data approaches for genomic sequence analysis and social network analysis Professor Rombo's publications demonstrate a strong focus on developing computational methods for biological network analysis, with particular emphasis on protein-protein interaction networks, molecular interaction networks, and lncRNA-disease association prediction. Her work bridges computer science and biology, creating practical tools and algorithms that address real biological challenges. Her scientific contributions include: Development of DIAMIN, a software library for distributed analysis of molecular interaction networks Creation of FEDRO, a tool for discovering candidate ORFs in plants with RNA editing Research on topological properties of biological networks and their functional implications Applications of network analysis to drug discovery and repurposing Professor Rombo actively teaches courses related to big data management and software engineering, mentoring students in computer science and artificial intelligence programs. Her office is located at Via Archirafi 34, Floor 2, Room 220 at UNIPA, with office hours on Mondays from 9:30 to 13:30.

Monica Mordoniniمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Autonomous Systems
• Computer Vision
• +۱۳ مورد دیگر

Monica Mordonini is an Associate Professor in the Department of Computer Engineering at the Faculty of Engineering, University of Parma, where she has been a faculty member since 1999. She teaches courses across multiple programs including Computer Engineering, Engineering Management, Civil and Environmental Engineering, and Law, with a focus on Artificial Intelligence, Software Engineering, and Information Systems. Her research interests lie primarily in Artificial Intelligence , with emphasis on visual sensors, unconventional vision algorithms for autonomous systems, and applications in video surveillance. More recently, her work extends to information extraction from distributed multimedia databases, semantic web applications, and ontology design for smart environments. She is actively involved in interdisciplinary research, particularly in healthcare technology, including patient tracking and operating room optimization using IoT and digital twin technologies. The most recent publications reflect a strong trend toward applied AI in healthcare and intelligent systems. Her work integrates computer vision, real-time data processing, and system modeling to improve clinical workflows and service delivery. Themes such as digital twins, location-based services, and simulation-based optimization are central to her current research trajectory. Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: There is no explicit information about PhD or Master’s students she has advised. Similarly, no grants or funded projects are detailed in the text, though her collaborative publications suggest involvement in research initiatives, particularly in healthcare and intelligent systems. Labs and Research Teams: While specific lab affiliations are not mentioned, Monica Mordonini collaborates extensively with researchers in healthcare, engineering, and computer science, particularly in projects involving patient tracking, operating room systems, and digital libraries. Her work appears to be conducted within interdisciplinary teams at the University of Parma, likely connected to intelligent systems and applied AI laboratories.

• Artificial Intelligence
• Deep Learning
• Natural Language Processing
• +۱۲ مورد دیگر

Gianfranco Lombardo is a Fixed-Term Researcher (RTD-A) at the Department of Engineering and Architecture, University of Parma, where he also earned his academic degrees. He has served as an Adjunct Professor and Research Fellow at the same institution and was a Visiting Researcher at the University of Florida's Herbert Wertheim College of Engineering. His educational background includes: Bachelor's in Computer, Electronics, and Telecommunications Engineering (2014), University of Parma Master's in Computer Engineering (2017), University of Parma Ph.D. in Information Technology (2021), University of Parma His research focuses on Artificial Intelligence , particularly Deep Learning , Natural Language Processing , and Knowledge Extraction from semi-structured data such as documents and social media. He applies these techniques in diverse domains including finance and healthcare. His work on predictive maintenance of LGVs using unsupervised learning and digital twins for operating rooms highlights his interdisciplinary approach. He is also involved in continual learning systems for location-based services. The recent publications (2024) demonstrate a strong trend in applying NLP and deep learning to financial data analysis (e.g., SEC filings, bankruptcy prediction) and healthcare optimization (e.g., digital twins for hospital operations). These works integrate multi-modal data and end-to-end architectures, showing a focus on practical, real-world applications of AI. He has taught courses such as Big Data and Business Intelligence , Programming of Mobile Systems , and Fundamentals of Computer Programming for Text Analysis across various programs including Information Systems Engineering, Data Science for Management, and Computer Engineering. He also served as a teacher tutor for Computer Engineering students. Professionally, he has worked as an AI consultant and as a Technical Consultant of Party (CTP) for the Genoa Court on software development matters. He is a registered professional engineer (Information Sector) in Crotone (No. 843).

Fabio Petrilloمشاهده پروفایل

دانشیار

• Empirical Software Engineering
• Software Quality
• Debugging
• +۴ مورد دیگر

Fabio Petrillo is an Associate Professor in the Department of Software Engineering and IT at École de Technologie Supérieure (ÉTS), Canada, since 2020. His research focuses on improving software engineers' lives through empirical studies and innovative techniques. Education: PhD in Computer Science (2016) and Master in Computer Science (2009) from Federal University of Rio Grande do Sul; Electrical Engineer (2001) from the same institution. Postdoctoral Fellow at Concordia University (2018). His research spans Empirical Software Engineering , Software Quality , Debugging , and Software Engineering for Computer Games . He pioneered Swarm Debugging , a collaborative debugging approach. His publications highlight trends in debugging techniques , cloud API compliance , and stream processing frameworks . He has contributed to datasets and models addressing software engineering challenges, though no formal scientific awards are mentioned. His work often involves collaborations with institutions like Concordia University and ÉTS.

• Human-AI Collaboration
• Artificial Intelligence
• Data Science
• +۳ مورد دیگر

Zhe Yu is an Assistant Professor in the Department of Software Engineering at Rochester Institute of Technology (RIT), leading the HIL-SE Lab since August 2020. Previously, he earned his Ph.D. in Computer Science from North Carolina State University, where he worked in the RAISE Lab under Dr. Tim Menzies, and holds an M.S. and B.S. from Shanghai Jiao Tong University in Control Science & Engineering and Automation, respectively. Education : Ph.D. (Computer Science, NC State), M.S. (Control Science & Engineering, SJTU), B.S. (Automation, SJTU) Leadership : Founded HIL-SE Lab at RIT Yu focuses on human-AI collaboration , advocating for AI systems that enhance human capabilities rather than replace them. His research emphasizes: Partnership models where humans and AI mutually validate outputs Optimizing human effort allocation through AI Quality assurance in human-AI workflows Applications in software engineering and data science He teaches courses including DSCI 633: Foundation of Data Science , IDAI 720: Research Methods for AI , and SWEN 352: Software Testing .

Alessandro Tasoraمشاهده پروفایل

استاد

• Theoretical Mechanics
• Numerical Methods in Multibody Dynamics
• Robotics
• +۷ مورد دیگر

Alessandro Tasora is a Full Professor at the University of Parma in the Department of Industrial Engineering and Department of Engineering and Architecture . He directs the Digital Dynamics Lab and serves as Scientific Director of the Smart Production Lab 4.0 . His work spans theoretical mechanics, robotics, and high-performance computing. Director, Digital Dynamics Lab (2019–present) Scientific Director, Smart Production Lab 4.0 (2017–present) National Scientific Qualification for Full Professor (2016) Honorary Associate, University of Wisconsin-Madison (2009–present) Research focuses on non-smooth multibody dynamics , GPU-accelerated simulation , and industrial robotics . He developed the Chrono::Engine software for multibody physics and HyperOCTANT for NLCP problems. His work addresses granular flows in nuclear reactors, vehicle mobility on deformable terrain, and historical structural analysis. Key article trends include GPU-based HPC for large-scale simulations, cone complementarity in contact dynamics, and isogeometric beam formulations for flexible bodies. Collaborations with Argonne National Laboratory and Fraunhofer ITWM highlight his international impact. Top-SNIP Paper (2017) International CAE Conference Poster Award (2015) Best Paper, Asian Conference on Multibody Dynamics (2010) TOP4 Paper, RAAD Robotics Workshop (2011) He has supervised over 40 theses in automation, tribology, and robotics. Grants include FFABR-MIUR , US Army RIF , and CNR projects . Projects involve seismic protection, autonomous AGVs, and Industry 4.0 consulting.

Federico Bergentiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Software Engineering
• Multi-agent Systems
• +۴ مورد دیگر

Federico Bergenti is an Associate Professor in Computer Science at the University of Parma, affiliated with the Department of Industrial Systems and Technologies Engineering (DISTI) and the Department of Mathematics, Physics, and Computer Science. He serves as Chair of the AI Lab since 2015 and is actively involved in teaching Artificial Intelligence, Software Engineering, and related courses across multiple degree programs. His educational background includes a Laurea degree (M.Sc.) in Electronic Engineering from the University of Parma (1998) and a Ph.D. in Information Technologies from the same institution (2002). Prior to his academic career, he worked at CSELT S.p.A. (1998-1999) and CNIT (2000-2006). Bergenti's research primarily focuses on Artificial Intelligence and Software Engineering, with special emphasis on multi-agent systems. His work spans agent communication languages, architectures for agent-based middleware, reusability in agent systems, and more recently, agent programming languages based on constraint logic programming. He is among the founders of the JADE initiative and remains active in the Agent-Oriented Software Engineering research community. Analysis of his recent publications reveals a consistent research trajectory centered on agent-oriented programming (particularly JADEScript), indoor positioning systems, neural-symbolic integration, and mathematical modeling of multi-agent dynamics using kinetic theory approaches. His work demonstrates strong interdisciplinary connections between computer science, mathematics, and engineering applications. Professionally, Bergenti has coordinated various scientific initiatives, served on the Senior Program Committee of the AAMAS international conference since 2006, hosted the IEEE WETICE conference in Parma in 2014, and served as Program Chair for WETICE 2016. He has participated in numerous European Commission-funded research projects under the 5th and 6th Framework Programmes. He leads the AI Lab at the University of Parma, where his team develops practical applications of agent-based systems, including indoor localization technologies, health assistance systems, and social network modeling. His research combines theoretical foundations with real-world implementations, particularly through the JADE multi-agent framework.